Salīdzināt metodes

	Kontrolkarte, ko palīdz simulācija	Statistiskā procesa vadība
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1920s (control charts); simulation integration from 1980s–1990s	1924–1931
Autors	Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators	Walter A. Shewhart
Tips	Hybrid quality monitoring method	Process monitoring and quality control method
Pirmavots	Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Citi nosaukumi	simulation-based SPC, Monte Carlo control chart design, simulation-enhanced SPC, virtual control chart	SPC, statistical quality control, process control charting, Shewhart control
Saistītās	6	6
Kopsavilkums	Simulation-assisted control chart integrates Monte Carlo or discrete-event simulation with traditional Shewhart-type control charting to design, validate, and optimize chart parameters before deployment on a real process. Rather than relying solely on assumed distributional forms, the practitioner builds a simulation model of the process, generates virtual data under in-control and out-of-control scenarios, and uses these runs to calibrate control limits, estimate average run length (ARL), and stress-test chart sensitivity — all without interrupting production.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.